The design and evaluation of the imaging system Clear-PEM for positron emission mammography, under development by the PEM Consortium within the framework of the Crystal Clear Collaboration at CERN, is presented. The proposed apparatus is based on fast, segmented, high atomic number radiation sensors with depth-of-interaction measurement capabilities, and state-of-the-art data acquisition techniques. The camera consists of two compact and planar detector heads with dimensions 16.5/spl times/14.5 cm/sup 2/ for breast and axilla imaging. Low-noise integrated electronics provide signal amplification and analog multiplexing based on a new data-driven architecture. The coincidence trigger and data acquisition architecture makes extensive use of pipeline processing structures and multi-event memories for high efficiency up to a data acquisition rate of one million events/s. Experimental validation of the detection techniques, namely the basic properties of the radiation sensors and the ability to measure the depth-of-interaction of the incoming photons, are presented. System performance in terms of detection sensitivity, count-rates and reconstructed image spatial resolution were also evaluated by means of a detailed Monte Carlo simulation and an iterative image reconstruction algorithm.

Reading room conditions such as illumination, ambient light, human factors and display luminance, play an important role on how radiologists analyze and interpret images. Indeed, serious diagnostic errors can appear when observing images through everyday monitors. Typically, these occur whenever professionals are ill-positioned with respect to the display or visualize images under improper light and luminance conditions. In this work, we show that virtual reality can assist radiodiagnostics by considerably diminishing or cancel out the effects of unsuitable ambient conditions. Our approach combines immersive head-mounted displays with interactive surfaces to support professional radiologists in analyzing medical images and formulating diagnostics. We evaluated our prototype with two senior medical doctors and four seasoned radiology fellows. Results indicate that our approach constitutes a viable, flexible, portable and cost-efficient option to traditional radiology reading rooms.

Glioblastoma (GB) is a malignant glioma associated with a mean overall survival of 12 to 18 months, even with optimal treatment, due to its high relapse rate and treatment resistance. The standardized first-line treatment consists of surgery, which allows for diagnosis and cytoreduction, followed by stereotactic fractionated radiotherapy and chemotherapy. Treatment failure can result from the poor passage of drugs through the blood–brain barrier (BBB). The development of novel and more effective therapeutic approaches is paramount to increasing the life expectancy of GB patients. Nanoparticle-based treatments include epitopes that are designed to interact with specialized transport systems, ultimately allowing the crossing of the BBB, increasing therapeutic efficacy, and reducing systemic toxicity and drug degradation. Polymeric nanoparticles have shown promising results in terms of precisely directing drugs to the brain with minimal systemic side effects. Various methods of drug delivery that pass through the BBB, such as the stereotactic injection of nanoparticles, are being actively tested in vitro and in vivo in animal models. A significant variety of pre-clinical studies with polymeric nanoparticles for the treatment of GB are being conducted, with only a few nanoparticle-based drug delivery systems to date having entered clinical trials. Pre-clinical studies are key to testing the safety and efficacy of these novel anticancer therapies and will hopefully facilitate the testing of the clinical validity of this promising treatment method. Here we review the recent literature concerning the most frequently reported types of nanoparticles for the treatment of GB.

Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant—which could be important to diminish reading time and improve accuracy—are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms—which may be able to allow screening programs customization both on periodicity and modality—are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome.

Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy—a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.